| Sample Names: |
|---|
| GSM2858677 |
| GSM2858679 |
| GSM2858681 |
| GSM2858684 |
| GSM2858685 |
| GSM2858687 |
| GSM2858689 |
| GSM2858691 |
| GSM2858693 |
| GSM2858695 |
| GSM2858696 |
| GSM2858698 |
| GSM2858700 |
| GSM2858703 |
| GSM2858708 |
| GSM2858709 |
| GSM2858710 |
| GSM2858714 |
| GSM2858715 |
| GSM2858717 |
| GSM2858720 |
| GSM2858721 |
| GSM2858723 |
| GSM2858724 |
| GSM2858725 |
| GSM2858733 |
| GSM2858737 |
| GSM2858738 |
| GSM2858740 |
| GSM2858741 |
| GSM2858745 |
| GSM2858747 |
| GSM2858750 |
| GSM2858751 |
| GSM2858753 |
| GSM2858755 |
| GSM2858756 |
| GSM2858758 |
| GSM2858759 |
| GSM2858763 |
| GSM2858764 |
| GSM2858768 |
| GSM2858769 |
| GSM2858771 |
| GSM2858774 |
| GSM2858776 |
| GSM2858779 |
| GSM2858781 |
| GSM2858782 |
| GSM2858783 |
| GSM2858786 |
| GSM2858787 |
| Sample Names: |
|---|
| GSM2858680 |
| GSM2858683 |
| GSM2858697 |
| GSM2858702 |
| GSM2858704 |
| GSM2858706 |
| GSM2858712 |
| GSM2858719 |
| GSM2858722 |
| GSM2858728 |
| GSM2858730 |
| GSM2858731 |
| GSM2858732 |
| GSM2858734 |
| GSM2858735 |
| GSM2858736 |
| GSM2858743 |
| GSM2858744 |
| GSM2858752 |
| GSM2858754 |
| GSM2858757 |
| GSM2858762 |
| GSM2858765 |
| GSM2858770 |
| GSM2858773 |
| GSM2858775 |
| GSM2858777 |
| GSM2858778 |
| GSM2858784 |
| GSM2858785 |
| GSM2858788 |
| GSM2858790 |
| GSM2858791 |
| GSM2858793 |
| GSM2858794 |
| GSM2858795 |
Note: A positive log fold change shows higher expression in the treatment group; a negative log fold change represents higher expression in the control group.
Here we show scatterplots comparing expression levels for all genes between the different samples, for i) all controls, ii) all treatment samples and iii) for all samples together.
These plots will only be produced when the total number of samples to compare within a group is less than or equal to 10.
BROWN: higher correlation; YELLOW: lower
This is a PCA plot of the count values following rlog normalization from the DESeq2 package:
## Warning: ggrepel: 42 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
The samples are shown in the 2D plane and distributed by their first two principal components. This type of plot is useful for visualizing the overall effect of experimental covariates and batch effects. It is also useful for identifying outlier samples. Control and treatment samples respectively may cluster together.
These boxplots show the distributions of count data before and after normalization (shown for normalization method DESeq2):
Representation of cpm unfiltered data:
Before normalization:
After normalization:
Variance of gene counts across samples are represented. Genes with lower variance than selected threshold (dashed grey line) were filtered out.
All counts were normalizated by DESeq2 algorithm. This count were scaled by log10 and plotted in a heatmap.
| GSM2858677 | GSM2858679 | GSM2858681 | GSM2858684 | GSM2858685 | GSM2858687 | GSM2858689 | GSM2858691 | GSM2858693 | GSM2858695 | GSM2858696 | GSM2858698 | GSM2858700 | GSM2858703 | GSM2858708 | GSM2858709 | GSM2858710 | GSM2858714 | GSM2858715 | GSM2858717 | GSM2858720 | GSM2858721 | GSM2858723 | GSM2858724 | GSM2858725 | GSM2858733 | GSM2858737 | GSM2858738 | GSM2858740 | GSM2858741 | GSM2858745 | GSM2858747 | GSM2858750 | GSM2858751 | GSM2858753 | GSM2858755 | GSM2858756 | GSM2858758 | GSM2858759 | GSM2858763 | GSM2858764 | GSM2858768 | GSM2858769 | GSM2858771 | GSM2858774 | GSM2858776 | GSM2858779 | GSM2858781 | GSM2858782 | GSM2858783 | GSM2858786 | GSM2858787 | GSM2858680 | GSM2858683 | GSM2858697 | GSM2858702 | GSM2858704 | GSM2858706 | GSM2858712 | GSM2858719 | GSM2858722 | GSM2858728 | GSM2858730 | GSM2858731 | GSM2858732 | GSM2858734 | GSM2858735 | GSM2858736 | GSM2858743 | GSM2858744 | GSM2858752 | GSM2858754 | GSM2858757 | GSM2858762 | GSM2858765 | GSM2858770 | GSM2858773 | GSM2858775 | GSM2858777 | GSM2858778 | GSM2858784 | GSM2858785 | GSM2858788 | GSM2858790 | GSM2858791 | GSM2858793 | GSM2858794 | GSM2858795 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4535 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 |
| 4536 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 |
| 4537 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 |
| 4540 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 |
| 4513 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.014 | 0.015 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.014 | 0.014 | 0.014 | 0.015 | 0.014 | 0.015 | 0.015 | 0.014 | 0.015 | 0.014 | 0.015 | 0.014 |
DEgenes Hunter uses multiple DE detection packages to analyse all genes in the input count table and labels them accordingly:
minpack_common argument.This barplot shows the total number of genes passing each stage of analysis - from the total number of genes in the input table of counts, to the genes surviving the expression filter, to the genes detected as DE by one package, to the genes detected by at least 1 packages.
This is the Venn Diagram of all possible DE genes (DEGs) according to at least on of the DE detection packages employed:
Benchmark of false positive calling:
Boxplot of FDR values among all genes with an FDR <= 0.05 in at least one DE detection package
## No Prevalent DEGs found, Bar charts of FDR values for prevalent genes cannot be shown
The complete results of the DEgenes Hunter differential expression analysis can be found in the “hunter_results_table.txt” file in the Common_results folder
Various plots specific to each package are shown below:
This plot compares the effective library size with raw library size
The effective library size is the factor used by DESeq2 normalizatioin algorithm for eahc sample. The effective library size must be dependent of raw library size.
This is the MA plot from DESeq2 package:
In DESeq2, the MA-plot (log ratio versus abundance) shows the log2 fold changes are attributable to a given variable over the mean of normalized counts. Points will be colored red if the adjusted Pvalue is less than 0.1. Points which fall out of the window are plotted as open triangles pointing either up or down.
A table containing the DESeq2 DEGs is provided: in Results_DESeq2/DEgenes_DESEq2.txt
A table containing the DESeq2 normalized counts is provided in Results_DESeq2/Normalized_counts_DESEq2.txt
Counts of prevalent DEGs were normalizated by DESeq2 algorithm. This count were scaled by log10 and plotted in a heatmap.
## Lower than 2 prevalent differential expression were found
WGCNA was run to look for modules (clusters) of coexpressed genes. These modules were then compared with the sample factors to look for correlation. If no sample factors were specified, this comparison was performed with treatment/control labels.
The following graphic shows the power value chosen for building clusters. The power is chosen by looking at the characteristics of the network produced.
In total there were 11 clusters. The following plot shows the number of genes per cluster:
The following plots show the correlation between the different modules and specified factors. This is done using eigengenes, which can be broadly thought of as the average expression pattern for the genes in a given cluster. MEn refers to the eigengene for cluster n.
This plot shows the correlation between clusters (eigen genes) and factors directly.This plot shows modules (black) and factors (green) as nodes. Correlations coefficients over 0.8 (red) and under -0.8 (blue) are represented as edges
This is an advanced section in order to compare the output of the packages used to perform data analysis. The data shown here does not necessarilly have any biological implication.
Distributions of p-values, unadjusted and adjusted for multiple testing (FDR)
Correlations of adjusted p-values, adjusted for multiple testing (FDR) and for log Fold Change.
First column contains the option names; second column contains the given values for each option in this run.
| opt | |
|---|---|
| minpack_common | 1 |
| p_val_cutoff | 0.05 |
| lfc | 1 |
| modules | DW |
| active_modules | 1 |